Overview

Dataset statistics

Number of variables22
Number of observations243840
Missing cells1534349
Missing cells (%)28.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.9 MiB
Average record size in memory176.0 B

Variable types

Numeric15
Categorical5
DateTime1
Unsupported1

Alerts

Unnamed: 0 is highly overall correlated with code and 4 other fieldsHigh correlation
code is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
latitude is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
longitude is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
no is highly overall correlated with no2 and 2 other fieldsHigh correlation
no2 is highly overall correlated with no and 2 other fieldsHigh correlation
nox is highly overall correlated with no and 2 other fieldsHigh correlation
nv10 is highly overall correlated with nv2.5 and 2 other fieldsHigh correlation
nv2.5 is highly overall correlated with nv10 and 2 other fieldsHigh correlation
o3 is highly overall correlated with no and 2 other fieldsHigh correlation
pm10 is highly overall correlated with nv10 and 3 other fieldsHigh correlation
pm2.5 is highly overall correlated with nv10 and 4 other fieldsHigh correlation
site is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
site_type is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
so2 is highly overall correlated with code and 4 other fieldsHigh correlation
v10 is highly overall correlated with pm10 and 2 other fieldsHigh correlation
v2.5 is highly overall correlated with pm2.5 and 1 other fieldsHigh correlation
co has 243840 (100.0%) missing valuesMissing
nox has 21737 (8.9%) missing valuesMissing
no2 has 21902 (9.0%) missing valuesMissing
no has 22020 (9.0%) missing valuesMissing
o3 has 74183 (30.4%) missing valuesMissing
so2 has 213135 (87.4%) missing valuesMissing
pm10 has 19228 (7.9%) missing valuesMissing
pm2.5 has 68498 (28.1%) missing valuesMissing
v10 has 184953 (75.9%) missing valuesMissing
v2.5 has 185662 (76.1%) missing valuesMissing
nv10 has 184953 (75.9%) missing valuesMissing
nv2.5 has 185662 (76.1%) missing valuesMissing
ws has 36192 (14.8%) missing valuesMissing
wd has 36192 (14.8%) missing valuesMissing
air_temp has 36192 (14.8%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
co is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2026-01-27 13:15:41.082519
Analysis finished2026-01-27 13:16:18.721471
Duration37.64 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Unique 

Distinct243840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1228900.8
Minimum863377
Maximum1545816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2026-01-27T13:16:18.817638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum863377
5-th percentile875568.95
Q1924336.75
median1423896.5
Q31484856.2
95-th percentile1533624.1
Maximum1545816
Range682439
Interquartile range (IQR)560519.5

Descriptive statistics

Standard deviation280830.2
Coefficient of variation (CV)0.22852145
Kurtosis-1.8921201
Mean1228900.8
Median Absolute Deviation (MAD)108408
Skewness-0.20730776
Sum2.9965517 × 1011
Variance7.8865599 × 1010
MonotonicityStrictly increasing
2026-01-27T13:16:18.941727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15458001
 
< 0.1%
15457991
 
< 0.1%
15457981
 
< 0.1%
15457971
 
< 0.1%
15457961
 
< 0.1%
15457951
 
< 0.1%
15457941
 
< 0.1%
15457931
 
< 0.1%
15457921
 
< 0.1%
15457911
 
< 0.1%
Other values (243830)243830
> 99.9%
ValueCountFrequency (%)
8633771
< 0.1%
8633781
< 0.1%
8633791
< 0.1%
8633801
< 0.1%
8633811
< 0.1%
8633821
< 0.1%
8633831
< 0.1%
8633841
< 0.1%
8633851
< 0.1%
8633861
< 0.1%
ValueCountFrequency (%)
15458161
< 0.1%
15458151
< 0.1%
15458141
< 0.1%
15458131
< 0.1%
15458121
< 0.1%
15458111
< 0.1%
15458101
< 0.1%
15458091
< 0.1%
15458081
< 0.1%
15458071
< 0.1%

site
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Bristol St Paul's
77568 
Birmingham A4540 Roadside
62760 
Bristol Temple Way
57864 
Birmingham Ladywood
45648 

Length

Max length25
Median length19
Mean length19.670768
Min length17

Characters and Unicode

Total characters4796520
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBirmingham A4540 Roadside
2nd rowBirmingham A4540 Roadside
3rd rowBirmingham A4540 Roadside
4th rowBirmingham A4540 Roadside
5th rowBirmingham A4540 Roadside

Common Values

ValueCountFrequency (%)
Bristol St Paul's77568
31.8%
Birmingham A4540 Roadside62760
25.7%
Bristol Temple Way57864
23.7%
Birmingham Ladywood45648
18.7%

Length

2026-01-27T13:16:19.063755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T13:16:19.156060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bristol135432
19.7%
birmingham108408
15.8%
st77568
11.3%
paul's77568
11.3%
a454062760
9.2%
roadside62760
9.2%
temple57864
8.4%
way57864
8.4%
ladywood45648
 
6.7%

Most occurring characters

ValueCountFrequency (%)
442032
 
9.2%
i415008
 
8.7%
a352248
 
7.3%
o289488
 
6.0%
s275760
 
5.7%
m274680
 
5.7%
l270864
 
5.6%
B243840
 
5.1%
r243840
 
5.1%
d216816
 
4.5%
Other values (20)1771944
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4796520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
442032
 
9.2%
i415008
 
8.7%
a352248
 
7.3%
o289488
 
6.0%
s275760
 
5.7%
m274680
 
5.7%
l270864
 
5.6%
B243840
 
5.1%
r243840
 
5.1%
d216816
 
4.5%
Other values (20)1771944
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4796520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
442032
 
9.2%
i415008
 
8.7%
a352248
 
7.3%
o289488
 
6.0%
s275760
 
5.7%
m274680
 
5.7%
l270864
 
5.6%
B243840
 
5.1%
r243840
 
5.1%
d216816
 
4.5%
Other values (20)1771944
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4796520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
442032
 
9.2%
i415008
 
8.7%
a352248
 
7.3%
o289488
 
6.0%
s275760
 
5.7%
m274680
 
5.7%
l270864
 
5.6%
B243840
 
5.1%
r243840
 
5.1%
d216816
 
4.5%
Other values (20)1771944
36.9%

code
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
BRS8
77568 
BIRR
62760 
BR11
57864 
BMLD
45648 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters975360
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIRR
2nd rowBIRR
3rd rowBIRR
4th rowBIRR
5th rowBIRR

Common Values

ValueCountFrequency (%)
BRS877568
31.8%
BIRR62760
25.7%
BR1157864
23.7%
BMLD45648
18.7%

Length

2026-01-27T13:16:19.270964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T13:16:19.352334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brs877568
31.8%
birr62760
25.7%
br1157864
23.7%
bmld45648
18.7%

Most occurring characters

ValueCountFrequency (%)
R260952
26.8%
B243840
25.0%
1115728
11.9%
877568
 
8.0%
S77568
 
8.0%
I62760
 
6.4%
M45648
 
4.7%
L45648
 
4.7%
D45648
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)975360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R260952
26.8%
B243840
25.0%
1115728
11.9%
877568
 
8.0%
S77568
 
8.0%
I62760
 
6.4%
M45648
 
4.7%
L45648
 
4.7%
D45648
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)975360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R260952
26.8%
B243840
25.0%
1115728
11.9%
877568
 
8.0%
S77568
 
8.0%
I62760
 
6.4%
M45648
 
4.7%
L45648
 
4.7%
D45648
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)975360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R260952
26.8%
B243840
25.0%
1115728
11.9%
877568
 
8.0%
S77568
 
8.0%
I62760
 
6.4%
M45648
 
4.7%
L45648
 
4.7%
D45648
 
4.7%

date
Date

Distinct77568
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2015-01-01 00:00:00
Maximum2023-11-06 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-27T13:16:19.463703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:19.609528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

co
Unsupported

Missing  Rejected  Unsupported 

Missing243840
Missing (%)100.0%
Memory size1.9 MiB

nox
Real number (ℝ)

High correlation  Missing 

Distinct203499
Distinct (%)91.6%
Missing21737
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean51.916861
Minimum-0.43031
Maximum1325.3703
Zeros1
Zeros (%)< 0.1%
Negative4
Negative (%)< 0.1%
Memory size1.9 MiB
2026-01-27T13:16:19.739853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.43031
5-th percentile5.981839
Q114.93357
median30.21721
Q362.32953
95-th percentile168.73573
Maximum1325.3703
Range1325.8006
Interquartile range (IQR)47.39596

Descriptive statistics

Standard deviation66.996042
Coefficient of variation (CV)1.2904486
Kurtosis28.077843
Mean51.916861
Median Absolute Deviation (MAD)18.78555
Skewness4.1390407
Sum11530890
Variance4488.4696
MonotonicityNot monotonic
2026-01-27T13:16:19.874352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.4912544
 
< 0.1%
11.47533
 
< 0.1%
10.327533
 
< 0.1%
12.622532
 
< 0.1%
8.6062532
 
< 0.1%
7.6532
 
< 0.1%
17.59531
 
< 0.1%
9.1831
 
< 0.1%
13.7730
 
< 0.1%
10.7129
 
< 0.1%
Other values (203489)221776
91.0%
(Missing)21737
 
8.9%
ValueCountFrequency (%)
-0.430311
< 0.1%
-0.191251
< 0.1%
-0.094711
< 0.1%
-0.047811
< 0.1%
01
< 0.1%
0.142061
< 0.1%
0.144411
< 0.1%
0.189181
< 0.1%
0.191252
< 0.1%
0.191371
< 0.1%
ValueCountFrequency (%)
1325.370311
< 0.1%
1309.897251
< 0.1%
1222.871151
< 0.1%
1194.930481
< 0.1%
1179.360431
< 0.1%
1130.76611
< 0.1%
1115.008541
< 0.1%
1095.986331
< 0.1%
1058.419061
< 0.1%
1043.617161
< 0.1%

no2
Real number (ℝ)

High correlation  Missing 

Distinct209002
Distinct (%)94.2%
Missing21902
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean26.835178
Minimum-0.52635
Maximum244.9109
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)< 0.1%
Memory size1.9 MiB
2026-01-27T13:16:20.000239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.52635
5-th percentile4.5302075
Q111.715193
median22.058145
Q337.506113
95-th percentile64.814242
Maximum244.9109
Range245.43725
Interquartile range (IQR)25.79092

Descriptive statistics

Standard deviation19.491217
Coefficient of variation (CV)0.72633084
Kurtosis1.8783143
Mean26.835178
Median Absolute Deviation (MAD)11.95197
Skewness1.2256473
Sum5955745.7
Variance379.90754
MonotonicityNot monotonic
2026-01-27T13:16:20.126552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.9887539
 
< 0.1%
8.6062535
 
< 0.1%
13.1962533
 
< 0.1%
6.6937531
 
< 0.1%
16.447531
 
< 0.1%
16.06530
 
< 0.1%
11.47530
 
< 0.1%
10.1362530
 
< 0.1%
8.797530
 
< 0.1%
12.622529
 
< 0.1%
Other values (208992)221620
90.9%
(Missing)21902
 
9.0%
ValueCountFrequency (%)
-0.526351
< 0.1%
-0.524981
< 0.1%
-0.474471
< 0.1%
-0.410041
< 0.1%
-0.343351
< 0.1%
-0.321491
< 0.1%
-0.241121
< 0.1%
-0.235021
< 0.1%
-0.220781
< 0.1%
-0.206691
< 0.1%
ValueCountFrequency (%)
244.91091
< 0.1%
240.422171
< 0.1%
180.474621
< 0.1%
178.089921
< 0.1%
176.253611
< 0.1%
173.579931
< 0.1%
172.594771
< 0.1%
165.733381
< 0.1%
160.81291
< 0.1%
160.772261
< 0.1%

no
Real number (ℝ)

High correlation  Missing 

Distinct179043
Distinct (%)80.7%
Missing22020
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean16.378972
Minimum-0.99577
Maximum704.65727
Zeros774
Zeros (%)0.3%
Negative535
Negative (%)0.2%
Memory size1.9 MiB
2026-01-27T13:16:20.244842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.99577
5-th percentile0.3962
Q11.532925
median4.58134
Q316.48204
95-th percentile69.350939
Maximum704.65727
Range705.65304
Interquartile range (IQR)14.949115

Descriptive statistics

Standard deviation34.102603
Coefficient of variation (CV)2.0820967
Kurtosis45.217667
Mean16.378972
Median Absolute Deviation (MAD)3.81021
Skewness5.4877993
Sum3633183.5
Variance1162.9875
MonotonicityNot monotonic
2026-01-27T13:16:20.374178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0774
 
0.3%
0.24946117
 
< 0.1%
0.3741996
 
< 0.1%
0.6236586
 
< 0.1%
0.1247385
 
< 0.1%
0.4989279
 
< 0.1%
0.7483869
 
< 0.1%
0.8731164
 
< 0.1%
1.3720363
 
< 0.1%
1.1225761
 
< 0.1%
Other values (179033)220326
90.4%
(Missing)22020
 
9.0%
ValueCountFrequency (%)
-0.995771
 
< 0.1%
-0.899611
 
< 0.1%
-0.74451
 
< 0.1%
-0.663851
 
< 0.1%
-0.623658
< 0.1%
-0.620421
 
< 0.1%
-0.6111813
< 0.1%
-0.5924710
< 0.1%
-0.5806217
< 0.1%
-0.574691
 
< 0.1%
ValueCountFrequency (%)
704.657271
< 0.1%
697.493471
< 0.1%
697.079941
< 0.1%
682.586171
< 0.1%
666.107471
< 0.1%
646.833251
< 0.1%
639.036071
< 0.1%
623.897281
< 0.1%
613.693411
< 0.1%
599.285061
< 0.1%

o3
Real number (ℝ)

High correlation  Missing 

Distinct31603
Distinct (%)18.6%
Missing74183
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean45.182264
Minimum-4.19097
Maximum179.56311
Zeros5
Zeros (%)< 0.1%
Negative70
Negative (%)< 0.1%
Memory size1.9 MiB
2026-01-27T13:16:20.495497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.19097
5-th percentile3.39269
Q127.84002
median46.35013
Q361.92972
95-th percentile82.48734
Maximum179.56311
Range183.75408
Interquartile range (IQR)34.0897

Descriptive statistics

Standard deviation24.449863
Coefficient of variation (CV)0.54113851
Kurtosis0.16646104
Mean45.182264
Median Absolute Deviation (MAD)16.81378
Skewness0.20126811
Sum7665487.3
Variance597.7958
MonotonicityNot monotonic
2026-01-27T13:16:20.617498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.59656176
 
0.1%
1.79613171
 
0.1%
1.9957162
 
0.1%
1.49678153
 
0.1%
1.39699151
 
0.1%
44.90325144
 
0.1%
1.89592141
 
0.1%
1.19742139
 
0.1%
1.94581137
 
0.1%
1.74624134
 
0.1%
Other values (31593)168149
69.0%
(Missing)74183
30.4%
ValueCountFrequency (%)
-4.190971
< 0.1%
-2.843871
< 0.1%
-2.382761
< 0.1%
-2.095491
< 0.1%
-1.622311
< 0.1%
-1.496781
< 0.1%
-1.197421
< 0.1%
-1.147532
< 0.1%
-1.13091
< 0.1%
-1.047742
< 0.1%
ValueCountFrequency (%)
179.563111
< 0.1%
178.216011
< 0.1%
177.168271
< 0.1%
176.868911
< 0.1%
173.675791
< 0.1%
173.62591
< 0.1%
173.576011
< 0.1%
173.426331
< 0.1%
172.97731
< 0.1%
172.677941
< 0.1%

so2
Real number (ℝ)

High correlation  Missing 

Distinct8754
Distinct (%)28.5%
Missing213135
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean0.85696149
Minimum-0.74638
Maximum15.56627
Zeros356
Zeros (%)0.1%
Negative57
Negative (%)< 0.1%
Memory size1.9 MiB
2026-01-27T13:16:20.737969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.74638
5-th percentile0.127414
Q10.28602
median0.47463
Q30.74584
95-th percentile3.77382
Maximum15.56627
Range16.31265
Interquartile range (IQR)0.45982

Descriptive statistics

Standard deviation1.3727398
Coefficient of variation (CV)1.6018688
Kurtosis20.612337
Mean0.85696149
Median Absolute Deviation (MAD)0.21244
Skewness4.1606309
Sum26313.002
Variance1.8844147
MonotonicityNot monotonic
2026-01-27T13:16:20.869361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0356
 
0.1%
0.47463279
 
0.1%
0.40682273
 
0.1%
0.54243260
 
0.1%
0.33902230
 
0.1%
0.67804221
 
0.1%
0.61023220
 
0.1%
0.27122161
 
0.1%
0.81364154
 
0.1%
0.74584151
 
0.1%
Other values (8744)28400
 
11.6%
(Missing)213135
87.4%
ValueCountFrequency (%)
-0.746384
< 0.1%
-0.678534
< 0.1%
-0.620881
 
< 0.1%
-0.618661
 
< 0.1%
-0.610683
< 0.1%
-0.542826
< 0.1%
-0.532182
 
< 0.1%
-0.474973
< 0.1%
-0.465661
 
< 0.1%
-0.407124
< 0.1%
ValueCountFrequency (%)
15.566271
 
< 0.1%
15.433221
 
< 0.1%
15.233652
< 0.1%
15.100611
 
< 0.1%
15.078431
 
< 0.1%
15.034091
 
< 0.1%
14.989741
 
< 0.1%
14.901043
< 0.1%
14.834522
< 0.1%
14.7681
 
< 0.1%

pm10
Real number (ℝ)

High correlation  Missing 

Distinct7983
Distinct (%)3.6%
Missing19228
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean16.032361
Minimum-4.831
Maximum276.925
Zeros96
Zeros (%)< 0.1%
Negative274
Negative (%)0.1%
Memory size1.9 MiB
2026-01-27T13:16:20.990165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.831
5-th percentile4.1
Q18.696
median13.4
Q320.29
95-th percentile36.8
Maximum276.925
Range281.756
Interquartile range (IQR)11.594

Descriptive statistics

Standard deviation11.316819
Coefficient of variation (CV)0.70587352
Kurtosis20.076876
Mean16.032361
Median Absolute Deviation (MAD)5.55
Skewness2.6949841
Sum3601060.8
Variance128.0704
MonotonicityNot monotonic
2026-01-27T13:16:21.109167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.4253505
 
1.4%
11.5943108
 
1.3%
10.6283079
 
1.3%
13.5273017
 
1.2%
12.5613013
 
1.2%
9.6623009
 
1.2%
14.4932965
 
1.2%
15.4592924
 
1.2%
8.6962856
 
1.2%
17.3922570
 
1.1%
Other values (7973)194566
79.8%
(Missing)19228
 
7.9%
ValueCountFrequency (%)
-4.8314
 
< 0.1%
-3.8654
 
< 0.1%
-3.61
 
< 0.1%
-3.41
 
< 0.1%
-3.31
 
< 0.1%
-31
 
< 0.1%
-2.92
 
< 0.1%
-2.89911
< 0.1%
-2.58
< 0.1%
-2.41
 
< 0.1%
ValueCountFrequency (%)
276.9251
< 0.1%
2621
< 0.1%
261.21
< 0.1%
257.0091
< 0.1%
234.2251
< 0.1%
231.8881
< 0.1%
229.9561
< 0.1%
225.31
< 0.1%
223.651
< 0.1%
221.4251
< 0.1%

pm2.5
Real number (ℝ)

High correlation  Missing 

Distinct6134
Distinct (%)3.5%
Missing68498
Missing (%)28.1%
Infinite0
Infinite (%)0.0%
Mean9.2560242
Minimum-6
Maximum235.779
Zeros860
Zeros (%)0.4%
Negative1130
Negative (%)0.5%
Memory size1.9 MiB
2026-01-27T13:16:21.230116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile1.887
Q14.2
median6.8
Q311.132
95-th percentile25.2
Maximum235.779
Range241.779
Interquartile range (IQR)6.932

Descriptive statistics

Standard deviation8.6349678
Coefficient of variation (CV)0.93290247
Kurtosis38.202965
Mean9.2560242
Median Absolute Deviation (MAD)3.05
Skewness3.8592115
Sum1622969.8
Variance74.562669
MonotonicityNot monotonic
2026-01-27T13:16:21.350782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64410
 
1.8%
54015
 
1.6%
43598
 
1.5%
73142
 
1.3%
33018
 
1.2%
82684
 
1.1%
22242
 
0.9%
92164
 
0.9%
101913
 
0.8%
111478
 
0.6%
Other values (6124)146678
60.2%
(Missing)68498
28.1%
ValueCountFrequency (%)
-61
 
< 0.1%
-59
 
< 0.1%
-432
 
< 0.1%
-3.81
 
< 0.1%
-3.61
 
< 0.1%
-3.31
 
< 0.1%
-3.11
 
< 0.1%
-399
< 0.1%
-2.81
 
< 0.1%
-2.71
 
< 0.1%
ValueCountFrequency (%)
235.7791
< 0.1%
2271
< 0.1%
226.7931
< 0.1%
2091
< 0.1%
2081
< 0.1%
205.3071
< 0.1%
2031
< 0.1%
1991
< 0.1%
197.0291
< 0.1%
1961
< 0.1%

v10
Real number (ℝ)

High correlation  Missing 

Distinct298
Distinct (%)0.5%
Missing184953
Missing (%)75.9%
Infinite0
Infinite (%)0.0%
Mean2.6942636
Minimum-9.4
Maximum30.2
Zeros691
Zeros (%)0.3%
Negative7412
Negative (%)3.0%
Memory size1.9 MiB
2026-01-27T13:16:21.464956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9.4
5-th percentile-1
Q10.8
median2.2
Q33.8
95-th percentile8.5
Maximum30.2
Range39.6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9860334
Coefficient of variation (CV)1.108293
Kurtosis4.9033317
Mean2.6942636
Median Absolute Deviation (MAD)1.5
Skewness1.5975061
Sum158657.1
Variance8.9163954
MonotonicityNot monotonic
2026-01-27T13:16:21.589767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.71214
 
0.5%
1.51202
 
0.5%
1.61175
 
0.5%
1.91170
 
0.5%
2.11150
 
0.5%
1.21139
 
0.5%
1.81137
 
0.5%
21108
 
0.5%
1.41103
 
0.5%
1.11099
 
0.5%
Other values (288)47390
 
19.4%
(Missing)184953
75.9%
ValueCountFrequency (%)
-9.41
 
< 0.1%
-8.21
 
< 0.1%
-7.91
 
< 0.1%
-7.61
 
< 0.1%
-71
 
< 0.1%
-6.41
 
< 0.1%
-6.21
 
< 0.1%
-5.92
< 0.1%
-5.81
 
< 0.1%
-5.53
< 0.1%
ValueCountFrequency (%)
30.21
< 0.1%
29.31
< 0.1%
28.61
< 0.1%
28.41
< 0.1%
28.21
< 0.1%
27.71
< 0.1%
27.21
< 0.1%
25.51
< 0.1%
25.22
< 0.1%
251
< 0.1%

v2.5
Real number (ℝ)

High correlation  Missing 

Distinct273
Distinct (%)0.5%
Missing185662
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean2.922371
Minimum-6.6
Maximum26.4
Zeros544
Zeros (%)0.2%
Negative4836
Negative (%)2.0%
Memory size1.9 MiB
2026-01-27T13:16:21.707999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.6
5-th percentile-0.5
Q11.2
median2.4
Q34
95-th percentile8.5
Maximum26.4
Range33
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.8194063
Coefficient of variation (CV)0.96476673
Kurtosis4.8729224
Mean2.922371
Median Absolute Deviation (MAD)1.4
Skewness1.6506656
Sum170017.7
Variance7.9490519
MonotonicityNot monotonic
2026-01-27T13:16:21.825294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.31251
 
0.5%
2.11215
 
0.5%
1.51200
 
0.5%
1.81195
 
0.5%
2.21193
 
0.5%
1.91190
 
0.5%
1.71181
 
0.5%
2.51178
 
0.5%
2.41176
 
0.5%
1.41172
 
0.5%
Other values (263)46227
 
19.0%
(Missing)185662
76.1%
ValueCountFrequency (%)
-6.61
 
< 0.1%
-6.21
 
< 0.1%
-5.61
 
< 0.1%
-5.41
 
< 0.1%
-5.31
 
< 0.1%
-5.11
 
< 0.1%
-4.91
 
< 0.1%
-4.61
 
< 0.1%
-4.45
< 0.1%
-4.13
< 0.1%
ValueCountFrequency (%)
26.41
 
< 0.1%
261
 
< 0.1%
25.91
 
< 0.1%
25.81
 
< 0.1%
25.61
 
< 0.1%
24.83
< 0.1%
24.11
 
< 0.1%
241
 
< 0.1%
23.61
 
< 0.1%
23.41
 
< 0.1%

nv10
Real number (ℝ)

High correlation  Missing 

Distinct788
Distinct (%)1.3%
Missing184953
Missing (%)75.9%
Infinite0
Infinite (%)0.0%
Mean13.029828
Minimum-6.1
Maximum256.8
Zeros54
Zeros (%)< 0.1%
Negative568
Negative (%)0.2%
Memory size1.9 MiB
2026-01-27T13:16:21.946190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.1
5-th percentile2.7
Q17
median10.8
Q316.4
95-th percentile30.9
Maximum256.8
Range262.9
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation9.6645495
Coefficient of variation (CV)0.74172501
Kurtosis21.039356
Mean13.029828
Median Absolute Deviation (MAD)4.4
Skewness2.7381888
Sum767287.5
Variance93.403517
MonotonicityNot monotonic
2026-01-27T13:16:22.067713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5445
 
0.2%
6.8427
 
0.2%
10.2422
 
0.2%
8.1420
 
0.2%
8.3417
 
0.2%
9.1410
 
0.2%
6.9406
 
0.2%
6.7405
 
0.2%
7.3404
 
0.2%
8.6403
 
0.2%
Other values (778)54728
 
22.4%
(Missing)184953
75.9%
ValueCountFrequency (%)
-6.11
 
< 0.1%
-4.21
 
< 0.1%
-41
 
< 0.1%
-3.84
< 0.1%
-3.72
 
< 0.1%
-3.62
 
< 0.1%
-3.52
 
< 0.1%
-3.45
< 0.1%
-3.35
< 0.1%
-3.23
< 0.1%
ValueCountFrequency (%)
256.81
< 0.1%
2241
< 0.1%
154.41
< 0.1%
121.51
< 0.1%
120.81
< 0.1%
1201
< 0.1%
119.91
< 0.1%
113.71
< 0.1%
113.21
< 0.1%
112.71
< 0.1%

nv2.5
Real number (ℝ)

High correlation  Missing 

Distinct674
Distinct (%)1.2%
Missing185662
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean8.5303036
Minimum-4.7
Maximum114.5
Zeros110
Zeros (%)< 0.1%
Negative1418
Negative (%)0.6%
Memory size1.9 MiB
2026-01-27T13:16:22.185653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.7
5-th percentile0.9
Q13.7
median6.3
Q310.6
95-th percentile24.1
Maximum114.5
Range119.2
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation8.0412895
Coefficient of variation (CV)0.94267331
Kurtosis12.472575
Mean8.5303036
Median Absolute Deviation (MAD)3.1
Skewness2.6981982
Sum496276
Variance64.662337
MonotonicityNot monotonic
2026-01-27T13:16:22.305599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7636
 
0.3%
5.4623
 
0.3%
4.5619
 
0.3%
4.2606
 
0.2%
4.4595
 
0.2%
4.3594
 
0.2%
3.8591
 
0.2%
4.8589
 
0.2%
5.3588
 
0.2%
3.5587
 
0.2%
Other values (664)52150
 
21.4%
(Missing)185662
76.1%
ValueCountFrequency (%)
-4.71
 
< 0.1%
-4.42
 
< 0.1%
-4.31
 
< 0.1%
-44
 
< 0.1%
-3.95
< 0.1%
-3.83
 
< 0.1%
-3.711
< 0.1%
-3.64
 
< 0.1%
-3.512
< 0.1%
-3.45
< 0.1%
ValueCountFrequency (%)
114.51
< 0.1%
110.31
< 0.1%
108.31
< 0.1%
104.91
< 0.1%
100.61
< 0.1%
99.41
< 0.1%
99.11
< 0.1%
97.31
< 0.1%
96.31
< 0.1%
961
< 0.1%

ws
Real number (ℝ)

Missing 

Distinct181
Distinct (%)0.1%
Missing36192
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean4.316029
Minimum0
Maximum19
Zeros83
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2026-01-27T13:16:22.551434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q12.6
median3.8
Q35.5
95-th percentile8.8
Maximum19
Range19
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.3432188
Coefficient of variation (CV)0.5429108
Kurtosis1.8009952
Mean4.316029
Median Absolute Deviation (MAD)1.4
Skewness1.1597399
Sum896214.8
Variance5.4906743
MonotonicityNot monotonic
2026-01-27T13:16:22.674984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34715
 
1.9%
2.74603
 
1.9%
2.94563
 
1.9%
3.14534
 
1.9%
2.84533
 
1.9%
2.64493
 
1.8%
3.24406
 
1.8%
2.34405
 
1.8%
3.34388
 
1.8%
2.54388
 
1.8%
Other values (171)162620
66.7%
(Missing)36192
 
14.8%
ValueCountFrequency (%)
083
 
< 0.1%
0.1230
 
0.1%
0.2248
 
0.1%
0.3326
 
0.1%
0.4391
0.2%
0.5508
0.2%
0.6516
0.2%
0.7595
0.2%
0.8683
0.3%
0.9841
0.3%
ValueCountFrequency (%)
192
 
< 0.1%
18.24
< 0.1%
18.12
 
< 0.1%
17.92
 
< 0.1%
17.86
< 0.1%
17.76
< 0.1%
17.51
 
< 0.1%
17.42
 
< 0.1%
17.23
< 0.1%
17.15
< 0.1%

wd
Real number (ℝ)

Missing 

Distinct3601
Distinct (%)1.7%
Missing36192
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean200.74839
Minimum0
Maximum360
Zeros18
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2026-01-27T13:16:22.800882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.9
Q1129.9
median221.1
Q3270.7
95-th percentile332.6
Maximum360
Range360
Interquartile range (IQR)140.8

Descriptive statistics

Standard deviation92.027904
Coefficient of variation (CV)0.45842413
Kurtosis-0.78307886
Mean200.74839
Median Absolute Deviation (MAD)60.2
Skewness-0.47484837
Sum41685001
Variance8469.1351
MonotonicityNot monotonic
2026-01-27T13:16:22.919766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226.6161
 
0.1%
220.2161
 
0.1%
224.3156
 
0.1%
223.5153
 
0.1%
221.2152
 
0.1%
221.1151
 
0.1%
225.9151
 
0.1%
222.8150
 
0.1%
227149
 
0.1%
223.6148
 
0.1%
Other values (3591)206116
84.5%
(Missing)36192
 
14.8%
ValueCountFrequency (%)
018
< 0.1%
0.129
< 0.1%
0.225
< 0.1%
0.335
< 0.1%
0.428
< 0.1%
0.530
< 0.1%
0.616
 
< 0.1%
0.742
< 0.1%
0.840
< 0.1%
0.925
< 0.1%
ValueCountFrequency (%)
36021
< 0.1%
359.924
< 0.1%
359.827
< 0.1%
359.732
< 0.1%
359.622
< 0.1%
359.528
< 0.1%
359.447
< 0.1%
359.335
< 0.1%
359.230
< 0.1%
359.123
< 0.1%

air_temp
Real number (ℝ)

Missing 

Distinct373
Distinct (%)0.2%
Missing36192
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean9.6376791
Minimum-7.2
Maximum30.6
Zeros330
Zeros (%)0.1%
Negative7443
Negative (%)3.1%
Memory size1.9 MiB
2026-01-27T13:16:23.042745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile0.8
Q15.8
median9.5
Q313.5
95-th percentile18.7
Maximum30.6
Range37.8
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation5.4898648
Coefficient of variation (CV)0.56962519
Kurtosis-0.20665961
Mean9.6376791
Median Absolute Deviation (MAD)3.9
Skewness0.11063139
Sum2001244.8
Variance30.138615
MonotonicityNot monotonic
2026-01-27T13:16:23.167705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.91567
 
0.6%
8.11564
 
0.6%
9.71547
 
0.6%
8.21544
 
0.6%
9.11538
 
0.6%
81521
 
0.6%
8.41515
 
0.6%
8.31504
 
0.6%
8.61496
 
0.6%
9.41496
 
0.6%
Other values (363)192356
78.9%
(Missing)36192
 
14.8%
ValueCountFrequency (%)
-7.21
 
< 0.1%
-7.12
 
< 0.1%
-71
 
< 0.1%
-6.72
 
< 0.1%
-6.52
 
< 0.1%
-6.41
 
< 0.1%
-6.35
< 0.1%
-6.22
 
< 0.1%
-6.16
< 0.1%
-69
< 0.1%
ValueCountFrequency (%)
30.61
 
< 0.1%
30.41
 
< 0.1%
30.23
< 0.1%
305
< 0.1%
29.94
< 0.1%
29.84
< 0.1%
29.75
< 0.1%
29.64
< 0.1%
29.53
< 0.1%
29.41
 
< 0.1%

latitude
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
51.462839
77568 
52.476145
62760 
51.457968
57864 
52.481346
45648 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2194560
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row52.476145
2nd row52.476145
3rd row52.476145
4th row52.476145
5th row52.476145

Common Values

ValueCountFrequency (%)
51.46283977568
31.8%
52.47614562760
25.7%
51.45796857864
23.7%
52.48134645648
18.7%

Length

2026-01-27T13:16:23.280998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T13:16:23.353952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
51.46283977568
31.8%
52.47614562760
25.7%
51.45796857864
23.7%
52.48134645648
18.7%

Most occurring characters

ValueCountFrequency (%)
5364464
16.6%
4352248
16.1%
1243840
11.1%
.243840
11.1%
6243840
11.1%
2185976
8.5%
8181080
8.3%
9135432
 
6.2%
3123216
 
5.6%
7120624
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5364464
16.6%
4352248
16.1%
1243840
11.1%
.243840
11.1%
6243840
11.1%
2185976
8.5%
8181080
8.3%
9135432
 
6.2%
3123216
 
5.6%
7120624
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5364464
16.6%
4352248
16.1%
1243840
11.1%
.243840
11.1%
6243840
11.1%
2185976
8.5%
8181080
8.3%
9135432
 
6.2%
3123216
 
5.6%
7120624
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5364464
16.6%
4352248
16.1%
1243840
11.1%
.243840
11.1%
6243840
11.1%
2185976
8.5%
8181080
8.3%
9135432
 
6.2%
3123216
 
5.6%
7120624
 
5.5%

longitude
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
-2.584482
77568 
-1.874978
62760 
-2.583975
57864 
-1.918235
45648 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters2194560
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.874978
2nd row-1.874978
3rd row-1.874978
4th row-1.874978
5th row-1.874978

Common Values

ValueCountFrequency (%)
-2.58448277568
31.8%
-1.87497862760
25.7%
-2.58397557864
23.7%
-1.91823545648
18.7%

Length

2026-01-27T13:16:23.451403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T13:16:23.530474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.58448277568
31.8%
1.87497862760
25.7%
2.58397557864
23.7%
1.91823545648
18.7%

Most occurring characters

ValueCountFrequency (%)
8384168
17.5%
2258648
11.8%
-243840
11.1%
.243840
11.1%
5238944
10.9%
4217896
9.9%
7183384
8.4%
9166272
7.6%
1154056
7.0%
3103512
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8384168
17.5%
2258648
11.8%
-243840
11.1%
.243840
11.1%
5238944
10.9%
4217896
9.9%
7183384
8.4%
9166272
7.6%
1154056
7.0%
3103512
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8384168
17.5%
2258648
11.8%
-243840
11.1%
.243840
11.1%
5238944
10.9%
4217896
9.9%
7183384
8.4%
9166272
7.6%
1154056
7.0%
3103512
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2194560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8384168
17.5%
2258648
11.8%
-243840
11.1%
.243840
11.1%
5238944
10.9%
4217896
9.9%
7183384
8.4%
9166272
7.6%
1154056
7.0%
3103512
 
4.7%

site_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Urban Background
123216 
Urban Traffic
120624 

Length

Max length16
Median length16
Mean length14.515945
Min length13

Characters and Unicode

Total characters3539568
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban Traffic
2nd rowUrban Traffic
3rd rowUrban Traffic
4th rowUrban Traffic
5th rowUrban Traffic

Common Values

ValueCountFrequency (%)
Urban Background123216
50.5%
Urban Traffic120624
49.5%

Length

2026-01-27T13:16:23.642869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T13:16:23.713487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urban243840
50.0%
background123216
25.3%
traffic120624
24.7%

Most occurring characters

ValueCountFrequency (%)
r487680
13.8%
a487680
13.8%
n367056
10.4%
U243840
 
6.9%
b243840
 
6.9%
243840
 
6.9%
c243840
 
6.9%
f241248
 
6.8%
B123216
 
3.5%
k123216
 
3.5%
Other values (6)734112
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3539568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r487680
13.8%
a487680
13.8%
n367056
10.4%
U243840
 
6.9%
b243840
 
6.9%
243840
 
6.9%
c243840
 
6.9%
f241248
 
6.8%
B123216
 
3.5%
k123216
 
3.5%
Other values (6)734112
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3539568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r487680
13.8%
a487680
13.8%
n367056
10.4%
U243840
 
6.9%
b243840
 
6.9%
243840
 
6.9%
c243840
 
6.9%
f241248
 
6.8%
B123216
 
3.5%
k123216
 
3.5%
Other values (6)734112
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3539568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r487680
13.8%
a487680
13.8%
n367056
10.4%
U243840
 
6.9%
b243840
 
6.9%
243840
 
6.9%
c243840
 
6.9%
f241248
 
6.8%
B123216
 
3.5%
k123216
 
3.5%
Other values (6)734112
20.7%

Interactions

2026-01-27T13:16:15.311835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:50.683516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:53.001657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:55.178414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:57.205621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.944093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.663203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:02.066787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.790199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.653399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:07.168761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.667293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-27T13:16:09.305557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-27T13:16:12.436691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:14.284027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.265459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-27T13:15:52.029526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:54.285585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:56.488875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.241525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:59.959338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.542770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.109744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.015506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:06.542034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.089358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.499420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:10.970653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:12.632040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:14.605660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.459462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:52.163449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:54.419224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:56.596096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.342053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.066010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.605904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.207956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.114302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:06.642862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.181856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.593165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:11.065790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:12.732336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:14.701356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.559805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:52.300153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:54.539035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:56.695971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.436184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.189929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.670515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.295928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.209470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:06.745743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.276669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.683084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:11.159867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:12.837037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:14.800247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.681083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:52.482023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:54.690145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:56.820255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.559281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.317033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.739999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.424833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.323437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:06.849298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.373198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.783749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:11.254706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:12.972680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:14.925433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.805914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:52.660764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:54.842832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:56.951827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.683857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.443554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.833973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.549684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.438797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:06.949137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.470054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.887546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:11.354670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:13.104112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:15.053967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:16.933940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:52.834971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:55.005743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:57.074138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:15:58.807118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:00.562682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:01.940085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:03.668558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:05.557302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:07.054670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:08.571914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:09.990191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:11.453867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:13.240526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-27T13:16:15.183154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-27T13:16:23.797260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0air_tempcodelatitudelongitudenono2noxnv10nv2.5o3pm10pm2.5sitesite_typeso2v10v2.5wdws
Unnamed: 01.0000.0530.9210.9210.921-0.085-0.025-0.063-0.069-0.0870.1990.164-0.0460.9210.8790.453-0.0390.141-0.0670.154
air_temp0.0531.0000.0420.0420.042-0.150-0.307-0.260-0.214-0.2300.281-0.140-0.2210.0420.0150.108-0.085-0.0920.0490.113
code0.9210.0421.0001.0001.0000.0710.2080.0990.0350.0780.1740.0990.0411.0001.0001.0000.1230.1340.0700.124
latitude0.9210.0421.0001.0001.0000.0710.2080.0990.0350.0780.1740.0990.0411.0001.0001.0000.1230.1340.0700.124
longitude0.9210.0421.0001.0001.0000.0710.2080.0990.0350.0780.1740.0990.0411.0001.0001.0000.1230.1340.0700.124
no-0.085-0.1500.0710.0710.0711.0000.8090.9160.3340.356-0.5930.3170.2460.0710.1040.0040.025-0.061-0.009-0.191
no2-0.025-0.3070.2080.2080.2080.8091.0000.9690.4260.473-0.7040.4230.4310.2080.3440.1870.1120.085-0.118-0.341
nox-0.063-0.2600.0990.0990.0990.9160.9691.0000.4120.455-0.7040.3980.3820.0990.1570.1630.0860.029-0.089-0.310
nv10-0.069-0.2140.0350.0350.0350.3340.4260.4121.0000.860-0.3040.9580.7950.0350.026NaN0.3340.306-0.214-0.250
nv2.5-0.087-0.2300.0780.0780.0780.3560.4730.4550.8601.000-0.3970.8390.9330.0780.107NaN0.3450.385-0.294-0.358
o30.1990.2810.1740.1740.174-0.593-0.704-0.704-0.304-0.3971.000-0.191-0.2890.1740.231-0.067-0.128-0.1130.0380.369
pm100.164-0.1400.0990.0990.0990.3170.4230.3980.9580.839-0.1911.0000.8250.0990.1000.1420.5550.453-0.244-0.217
pm2.5-0.046-0.2210.0410.0410.0410.2460.4310.3820.7950.933-0.2890.8251.0000.0410.0120.1270.5010.641-0.276-0.338
site0.9210.0421.0001.0001.0000.0710.2080.0990.0350.0780.1740.0990.0411.0001.0001.0000.1230.1340.0700.124
site_type0.8790.0151.0001.0001.0000.1040.3440.1570.0260.1070.2310.1000.0121.0001.0001.0000.0620.1680.0160.037
so20.4530.1081.0001.0001.0000.0040.1870.163NaNNaN-0.0670.1420.1271.0001.0001.000NaNNaN-0.029-0.124
v10-0.039-0.0850.1230.1230.1230.0250.1120.0860.3340.345-0.1280.5550.5010.1230.062NaN1.0000.700-0.331-0.194
v2.50.141-0.0920.1340.1340.134-0.0610.0850.0290.3060.385-0.1130.4530.6410.1340.168NaN0.7001.000-0.353-0.196
wd-0.0670.0490.0700.0700.070-0.009-0.118-0.089-0.214-0.2940.038-0.244-0.2760.0700.016-0.029-0.331-0.3531.0000.071
ws0.1540.1130.1240.1240.124-0.191-0.341-0.310-0.250-0.3580.369-0.217-0.3380.1240.037-0.124-0.194-0.1960.0711.000

Missing values

2026-01-27T13:16:17.142994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-27T13:16:17.526085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-27T13:16:18.298288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0sitecodedateconoxno2noo3so2pm10pm2.5v10v2.5nv10nv2.5wswdair_templatitudelongitudesite_type
0863377Birmingham A4540 RoadsideBIRR2016-09-09 00:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
1863378Birmingham A4540 RoadsideBIRR2016-09-09 01:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
2863379Birmingham A4540 RoadsideBIRR2016-09-09 02:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
3863380Birmingham A4540 RoadsideBIRR2016-09-09 03:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
4863381Birmingham A4540 RoadsideBIRR2016-09-09 04:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
5863382Birmingham A4540 RoadsideBIRR2016-09-09 05:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
6863383Birmingham A4540 RoadsideBIRR2016-09-09 06:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
7863384Birmingham A4540 RoadsideBIRR2016-09-09 07:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
8863385Birmingham A4540 RoadsideBIRR2016-09-09 08:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
9863386Birmingham A4540 RoadsideBIRR2016-09-09 09:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN52.476145-1.874978Urban Traffic
Unnamed: 0sitecodedateconoxno2noo3so2pm10pm2.5v10v2.5nv10nv2.5wswdair_templatitudelongitudesite_type
2438301545807Bristol Temple WayBR112023-11-06 14:00:00NaN35.1900023.523757.60853NaNNaN10.628NaNNaNNaNNaNNaN6.1269.410.351.457968-2.583975Urban Traffic
2438311545808Bristol Temple WayBR112023-11-06 15:00:00NaN44.3700029.261259.85367NaNNaN15.459NaNNaNNaNNaNNaN5.1270.19.451.457968-2.583975Urban Traffic
2438321545809Bristol Temple WayBR112023-11-06 16:00:00NaN59.6700039.3975013.22138NaNNaN15.459NaNNaNNaNNaNNaN4.3269.88.651.457968-2.583975Urban Traffic
2438331545810Bristol Temple WayBR112023-11-06 17:00:00NaN38.2500028.878756.11177NaNNaN10.628NaNNaNNaNNaNNaN3.9274.28.151.457968-2.583975Urban Traffic
2438341545811Bristol Temple WayBR112023-11-06 18:00:00NaN56.0362535.9550013.09665NaNNaN15.459NaNNaNNaNNaNNaN3.8269.77.851.457968-2.583975Urban Traffic
2438351545812Bristol Temple WayBR112023-11-06 19:00:00NaN38.4412525.818758.23218NaNNaN14.493NaNNaNNaNNaNNaN4.1264.87.651.457968-2.583975Urban Traffic
2438361545813Bristol Temple WayBR112023-11-06 20:00:00NaN26.3925020.272503.99136NaNNaN12.561NaNNaNNaNNaNNaN4.2265.77.551.457968-2.583975Urban Traffic
2438371545814Bristol Temple WayBR112023-11-06 21:00:00NaN33.8512523.906256.48596NaNNaN12.561NaNNaNNaNNaNNaN3.8261.17.251.457968-2.583975Urban Traffic
2438381545815Bristol Temple WayBR112023-11-06 22:00:00NaN37.8675025.245008.23218NaNNaN11.594NaNNaNNaNNaNNaN3.8244.56.951.457968-2.583975Urban Traffic
2438391545816Bristol Temple WayBR112023-11-06 23:00:00NaN23.5237514.726255.73758NaNNaN10.628NaNNaNNaNNaNNaN4.0253.77.951.457968-2.583975Urban Traffic